Monocular 3D lane detection has become a fundamental problem in the context of autonomous driving, which comprises the tasks of finding the road surface and locating lane markings. One major challenge lies in a flexible but robust line representation capable of modeling complex lane structures, while still avoiding unpredictable behavior. While previous methods rely on fully data-driven approaches, we instead introduce a novel approach LaneCPP that uses a continuous 3D lane detection model leveraging physical prior knowledge about the lane structure and road geometry. While our sophisticated lane model is capable of modeling complex road structures, it also shows robust behavior since physical constraints are incorporated by means of a regularization scheme that can be analytically applied to our parametric representation. Moreover, we incorporate prior knowledge about the road geometry into the 3D feature space by modeling geometry-aware spatial features, guiding the network to learn an internal road surface representation. In our experiments, we show the benefits of our contributions and prove the meaningfulness of using priors to make 3D lane detection more robust. The results show that LaneCPP achieves state-of-the-art performance in terms of F-Score and geometric errors.

Monocular 3D traffic line detection jointly tackles the detection of lane markings and regression of their 3D location. The greatest challenge is the exact estimation of various line shapes in the world, which highly depends on the chosen representation. While anchor-based and grid-based line representations have been proposed, all suffer from the same limitation, the necessity of discretizing the 3D space. To address this limitation, we present an anchor-free parametric lane representation, which defines traffic lines as continuous curves in 3D space. Choosing splines as our representation, we show their superiority over polynomials of different degrees that were proposed in previous 2D lane detection approaches. Our continuous representation allows us to model even complex lane shapes at any position in the 3D space, while implicitly enforcing smoothness constraints. Our model is validated on a synthetic 3D lane dataset including a variety of scenes in terms of complexity of road shape and illumination. We outperform the state-of-the-art in nearly all geometric performance metrics and achieve a great leap in the detection rate. In contrast to discrete representations, our parametric model requires no post-processing achieving highest processing speed. Additionally, we provide a thorough analysis over different parametric representations for 3D lane detection. The code and trained models are available on our project website https://3d-splinenet.github.io/.